School of Transportation, Southeast University, Nanjing 210096, China.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China.
Sensors (Basel). 2022 Jul 14;22(14):5263. doi: 10.3390/s22145263.
Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.
交通状态预测为智能交通系统（ITSs）提供了关键信息，用于主动交通管理，其重要性使得该领域的研究论文数量巨大。在过去的几十年中，分解-重构（DR）混合模型受到了众多研究人员的青睐，为 ITSs 的短期交通状态预测提供了更强大的框架。本研究调查了过去大约二十年来，基于 DR 的短期交通状态预测工作，特别是重点关注如何利用分解和重构策略来提高交通参数基本预测模型的可预测性和可解释性。报告的基于 DR 的模型进行了分类，并仔细研究了它们在该领域的应用。还提供了讨论和潜在的未来方向，以支持更复杂的应用。这项工作为建模人员提供了建议，并帮助他们在研究和应用中选择合适的分解和重构策略。
